Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations2592
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory749.4 KiB
Average record size in memory296.1 B

Variable types

Text5
Categorical17
Numeric15

Alerts

Component has constant value "L103" Constant
CD has constant value "0" Constant
NII has constant value "0" Constant
NL has constant value "0" Constant
NLE has constant value "0" Constant
TCD has constant value "0" Constant
WarningBlocker has constant value "0" Constant
WarningCritical has constant value "0" Constant
WarningMajor has constant value "0" Constant
WarningMinor has constant value "0" Constant
Coupling Metric Rules has constant value "0" Constant
Documentation Metric Rules has constant value "0" Constant
Anti Pattern is highly overall correlated with Complexity Metric Rules and 3 other fieldsHigh correlation
CLOC is highly overall correlated with DLOC and 3 other fieldsHigh correlation
Complexity Metric Rules is highly overall correlated with Anti Pattern and 4 other fieldsHigh correlation
DLOC is highly overall correlated with CLOC and 1 other fieldsHigh correlation
EndLine is highly overall correlated with LineHigh correlation
LLOC is highly overall correlated with LOC and 6 other fieldsHigh correlation
LOC is highly overall correlated with CLOC and 8 other fieldsHigh correlation
Line is highly overall correlated with EndLineHigh correlation
McCC is highly overall correlated with Anti Pattern and 2 other fieldsHigh correlation
NOS is highly overall correlated with Complexity Metric Rules and 6 other fieldsHigh correlation
NUMPAR is highly overall correlated with Anti Pattern and 1 other fieldsHigh correlation
Size Metric Rules is highly overall correlated with LLOC and 5 other fieldsHigh correlation
TCLOC is highly overall correlated with CLOC and 3 other fieldsHigh correlation
TLLOC is highly overall correlated with LLOC and 6 other fieldsHigh correlation
TLOC is highly overall correlated with CLOC and 8 other fieldsHigh correlation
TNOS is highly overall correlated with Complexity Metric Rules and 6 other fieldsHigh correlation
WarningInfo is highly overall correlated with Anti Pattern and 9 other fieldsHigh correlation
Column is highly imbalanced (79.1%) Imbalance
WarningInfo is highly imbalanced (91.9%) Imbalance
Anti Pattern is highly imbalanced (92.3%) Imbalance
Complexity Metric Rules is highly imbalanced (93.2%) Imbalance
Size Metric Rules is highly imbalanced (95.1%) Imbalance
ID has unique values Unique
CLOC has 1723 (66.5%) zeros Zeros
DLOC has 1978 (76.3%) zeros Zeros
NOI has 2154 (83.1%) zeros Zeros
TCLOC has 1707 (65.9%) zeros Zeros

Reproduction

Analysis started2024-10-22 17:17:23.843631
Analysis finished2024-10-22 17:17:42.818267
Duration18.97 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

ID
Text

Unique 

Distinct2592
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:43.047728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length5.4691358
Min length4

Characters and Unicode

Total characters14176
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2592 ?
Unique (%)100.0%

Sample

1st rowL184
2nd rowL197
3rd rowL206
4th rowL212
5th rowL220
ValueCountFrequency (%)
l15772 1
 
< 0.1%
l16052 1
 
< 0.1%
l16281 1
 
< 0.1%
l16285 1
 
< 0.1%
l16564 1
 
< 0.1%
l16573 1
 
< 0.1%
l16582 1
 
< 0.1%
l16590 1
 
< 0.1%
l16620 1
 
< 0.1%
l16629 1
 
< 0.1%
Other values (2582) 2582
99.6%
2024-10-22T20:17:43.422409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 2592
18.3%
1 2246
15.8%
3 1184
8.4%
4 1127
8.0%
6 1115
7.9%
7 1067
7.5%
2 1057
7.5%
5 1035
 
7.3%
8 967
 
6.8%
0 929
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11584
81.7%
Uppercase Letter 2592
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2246
19.4%
3 1184
10.2%
4 1127
9.7%
6 1115
9.6%
7 1067
9.2%
2 1057
9.1%
5 1035
8.9%
8 967
8.3%
0 929
8.0%
9 857
 
7.4%
Uppercase Letter
ValueCountFrequency (%)
L 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11584
81.7%
Latin 2592
 
18.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2246
19.4%
3 1184
10.2%
4 1127
9.7%
6 1115
9.6%
7 1067
9.2%
2 1057
9.1%
5 1035
8.9%
8 967
8.3%
0 929
8.0%
9 857
 
7.4%
Latin
ValueCountFrequency (%)
L 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 2592
18.3%
1 2246
15.8%
3 1184
8.4%
4 1127
8.0%
6 1115
7.9%
7 1067
7.5%
2 1057
7.5%
5 1035
 
7.3%
8 967
 
6.8%
0 929
 
6.6%

Name
Text

Distinct1592
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:43.633677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length80
Median length59
Mean length20.72338
Min length2

Characters and Unicode

Total characters53715
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1373 ?
Unique (%)53.0%

Sample

1st rowready
2nd row_reject
3rd rowauthenticate
4th rowauthenticate_header
5th rowauthenticate
ValueCountFrequency (%)
init 118
 
4.6%
setup 115
 
4.4%
get 82
 
3.2%
post 36
 
1.4%
to_representation 33
 
1.3%
setup_method 33
 
1.3%
to_internal_value 32
 
1.2%
render 21
 
0.8%
has_permission 15
 
0.6%
get_serializer 14
 
0.5%
Other values (1560) 2093
80.7%
2024-10-22T20:17:43.979533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 7334
13.7%
_ 6956
12.9%
t 6720
12.5%
s 4006
 
7.5%
i 3324
 
6.2%
r 2989
 
5.6%
a 2892
 
5.4%
n 2666
 
5.0%
o 2455
 
4.6%
l 2074
 
3.9%
Other values (44) 12299
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46459
86.5%
Connector Punctuation 6956
 
12.9%
Uppercase Letter 213
 
0.4%
Decimal Number 87
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7334
15.8%
t 6720
14.5%
s 4006
 
8.6%
i 3324
 
7.2%
r 2989
 
6.4%
a 2892
 
6.2%
n 2666
 
5.7%
o 2455
 
5.3%
l 2074
 
4.5%
d 1825
 
3.9%
Other values (16) 10174
21.9%
Uppercase Letter
ValueCountFrequency (%)
U 121
56.8%
D 20
 
9.4%
T 15
 
7.0%
P 11
 
5.2%
E 7
 
3.3%
O 7
 
3.3%
C 6
 
2.8%
A 6
 
2.8%
S 6
 
2.8%
F 2
 
0.9%
Other values (7) 12
 
5.6%
Decimal Number
ValueCountFrequency (%)
4 21
24.1%
0 19
21.8%
2 17
19.5%
6 7
 
8.0%
3 6
 
6.9%
8 5
 
5.7%
1 4
 
4.6%
5 3
 
3.4%
7 3
 
3.4%
9 2
 
2.3%
Connector Punctuation
ValueCountFrequency (%)
_ 6956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46672
86.9%
Common 7043
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7334
15.7%
t 6720
14.4%
s 4006
 
8.6%
i 3324
 
7.1%
r 2989
 
6.4%
a 2892
 
6.2%
n 2666
 
5.7%
o 2455
 
5.3%
l 2074
 
4.4%
d 1825
 
3.9%
Other values (33) 10387
22.3%
Common
ValueCountFrequency (%)
_ 6956
98.8%
4 21
 
0.3%
0 19
 
0.3%
2 17
 
0.2%
6 7
 
0.1%
3 6
 
0.1%
8 5
 
0.1%
1 4
 
0.1%
5 3
 
< 0.1%
7 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7334
13.7%
_ 6956
12.9%
t 6720
12.5%
s 4006
 
7.5%
i 3324
 
6.2%
r 2989
 
5.6%
a 2892
 
5.4%
n 2666
 
5.0%
o 2455
 
4.6%
l 2074
 
3.9%
Other values (44) 12299
22.9%
Distinct2585
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:44.130464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length153
Median length105
Mean length59.863426
Min length20

Characters and Unicode

Total characters155166
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2579 ?
Unique (%)99.5%

Sample

1st rowapps.RestFrameworkConfig.ready~Fn
2nd rowauthentication.CSRFCheck._reject~Fn
3rd rowauthentication.BaseAuthentication.authenticate~Fn
4th rowauthentication.BaseAuthentication.authenticate_header~Fn
5th rowauthentication.BasicAuthentication.authenticate~Fn
ValueCountFrequency (%)
inspectors.viewinspector.view~fn 3
 
0.1%
test_coreapi.mockapiview.test_get~fn 2
 
0.1%
test_coreapi.mockapiview.test_put~fn 2
 
0.1%
request.request.user~fn 2
 
0.1%
test_coreapi.mockapiview.test_patch~fn 2
 
0.1%
test_coreapi.mockapiview.test_delete~fn 2
 
0.1%
test_coreapi.mockapiview.test_post~fn 2
 
0.1%
test_coreapi.mockapiview.test_foo~fn 2
 
0.1%
fields.field.validators~fn 2
 
0.1%
request.request.auth~fn 2
 
0.1%
Other values (2569) 2571
99.2%
2024-10-22T20:17:44.416683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 20844
13.4%
t 17211
 
11.1%
s 12443
 
8.0%
i 10183
 
6.6%
_ 9686
 
6.2%
n 9495
 
6.1%
r 8437
 
5.4%
a 7665
 
4.9%
o 6262
 
4.0%
l 5669
 
3.7%
Other values (55) 47271
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 124616
80.3%
Uppercase Letter 12555
 
8.1%
Connector Punctuation 9686
 
6.2%
Other Punctuation 5493
 
3.5%
Math Symbol 2592
 
1.7%
Decimal Number 224
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20844
16.7%
t 17211
13.8%
s 12443
10.0%
i 10183
8.2%
n 9495
7.6%
r 8437
 
6.8%
a 7665
 
6.2%
o 6262
 
5.0%
l 5669
 
4.5%
d 4461
 
3.6%
Other values (16) 21946
17.6%
Uppercase Letter
ValueCountFrequency (%)
F 3221
25.7%
T 2041
16.3%
S 806
 
6.4%
P 611
 
4.9%
R 598
 
4.8%
A 588
 
4.7%
V 561
 
4.5%
M 510
 
4.1%
I 491
 
3.9%
C 419
 
3.3%
Other values (16) 2709
21.6%
Decimal Number
ValueCountFrequency (%)
4 44
19.6%
0 40
17.9%
2 33
14.7%
5 21
9.4%
6 20
8.9%
7 20
8.9%
1 18
8.0%
8 14
 
6.2%
3 11
 
4.9%
9 3
 
1.3%
Connector Punctuation
ValueCountFrequency (%)
_ 9686
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5493
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 137171
88.4%
Common 17995
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20844
15.2%
t 17211
12.5%
s 12443
 
9.1%
i 10183
 
7.4%
n 9495
 
6.9%
r 8437
 
6.2%
a 7665
 
5.6%
o 6262
 
4.6%
l 5669
 
4.1%
d 4461
 
3.3%
Other values (42) 34501
25.2%
Common
ValueCountFrequency (%)
_ 9686
53.8%
. 5493
30.5%
~ 2592
 
14.4%
4 44
 
0.2%
0 40
 
0.2%
2 33
 
0.2%
5 21
 
0.1%
6 20
 
0.1%
7 20
 
0.1%
1 18
 
0.1%
Other values (3) 28
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20844
13.4%
t 17211
 
11.1%
s 12443
 
8.0%
i 10183
 
6.6%
_ 9686
 
6.2%
n 9495
 
6.1%
r 8437
 
5.4%
a 7665
 
4.9%
o 6262
 
4.0%
l 5669
 
3.7%
Other values (55) 47271
30.5%

Parent
Text

Distinct767
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:44.629584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length5.4683642
Min length4

Characters and Unicode

Total characters14174
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique299 ?
Unique (%)11.5%

Sample

1st rowL177
2nd rowL195
3rd rowL203
4th rowL203
5th rowL216
ValueCountFrequency (%)
l7804 35
 
1.4%
l6242 33
 
1.3%
l4302 28
 
1.1%
l3716 23
 
0.9%
l902 21
 
0.8%
l8449 20
 
0.8%
l12889 20
 
0.8%
l4701 20
 
0.8%
l4566 19
 
0.7%
l17208 19
 
0.7%
Other values (757) 2354
90.8%
2024-10-22T20:17:44.956090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 2592
18.3%
1 2302
16.2%
6 1190
8.4%
2 1134
8.0%
3 1084
7.6%
4 1051
7.4%
8 1017
 
7.2%
7 1016
 
7.2%
0 976
 
6.9%
5 967
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11582
81.7%
Uppercase Letter 2592
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2302
19.9%
6 1190
10.3%
2 1134
9.8%
3 1084
9.4%
4 1051
9.1%
8 1017
8.8%
7 1016
8.8%
0 976
8.4%
5 967
8.3%
9 845
 
7.3%
Uppercase Letter
ValueCountFrequency (%)
L 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11582
81.7%
Latin 2592
 
18.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2302
19.9%
6 1190
10.3%
2 1134
9.8%
3 1084
9.4%
4 1051
9.1%
8 1017
8.8%
7 1016
8.8%
0 976
8.4%
5 967
8.3%
9 845
 
7.3%
Latin
ValueCountFrequency (%)
L 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 2592
18.3%
1 2302
16.2%
6 1190
8.4%
2 1134
8.0%
3 1084
7.6%
4 1051
7.4%
8 1017
 
7.2%
7 1016
 
7.2%
0 976
 
6.9%
5 967
 
6.8%

Component
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
L103
2592 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters10368
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL103
2nd rowL103
3rd rowL103
4th rowL103
5th rowL103

Common Values

ValueCountFrequency (%)
L103 2592
100.0%

Length

2024-10-22T20:17:45.062812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:45.133613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
l103 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
L 2592
25.0%
1 2592
25.0%
0 2592
25.0%
3 2592
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7776
75.0%
Uppercase Letter 2592
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2592
33.3%
0 2592
33.3%
3 2592
33.3%
Uppercase Letter
ValueCountFrequency (%)
L 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7776
75.0%
Latin 2592
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2592
33.3%
0 2592
33.3%
3 2592
33.3%
Latin
ValueCountFrequency (%)
L 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 2592
25.0%
1 2592
25.0%
0 2592
25.0%
3 2592
25.0%

Path
Text

Distinct109
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:45.283808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length95
Median length76
Mean length57.159722
Min length45

Characters and Unicode

Total characters148158
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row/content/django-rest-framework/rest_framework/apps.py
2nd row/content/django-rest-framework/rest_framework/authentication.py
3rd row/content/django-rest-framework/rest_framework/authentication.py
4th row/content/django-rest-framework/rest_framework/authentication.py
5th row/content/django-rest-framework/rest_framework/authentication.py
ValueCountFrequency (%)
content/django-rest-framework/tests/test_fields.py 141
 
5.4%
content/django-rest-framework/rest_framework/fields.py 118
 
4.6%
content/django-rest-framework/tests/test_renderers.py 98
 
3.8%
content/django-rest-framework/tests/schemas/test_coreapi.py 81
 
3.1%
content/django-rest-framework/tests/test_routers.py 78
 
3.0%
content/django-rest-framework/tests/test_serializer.py 78
 
3.0%
content/django-rest-framework/tests/test_relations.py 74
 
2.9%
content/django-rest-framework/rest_framework/serializers.py 71
 
2.7%
content/django-rest-framework/tests/schemas/test_openapi.py 69
 
2.7%
content/django-rest-framework/tests/test_pagination.py 65
 
2.5%
Other values (99) 1719
66.3%
2024-10-22T20:17:45.556209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 17783
12.0%
e 16877
11.4%
r 12698
 
8.6%
s 11892
 
8.0%
/ 10783
 
7.3%
o 10033
 
6.8%
n 9527
 
6.4%
a 8196
 
5.5%
- 5184
 
3.5%
m 4047
 
2.7%
Other values (20) 41138
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 126609
85.5%
Other Punctuation 13375
 
9.0%
Dash Punctuation 5184
 
3.5%
Connector Punctuation 2990
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 17783
14.0%
e 16877
13.3%
r 12698
10.0%
s 11892
9.4%
o 10033
 
7.9%
n 9527
 
7.5%
a 8196
 
6.5%
m 4047
 
3.2%
f 3694
 
2.9%
w 3563
 
2.8%
Other values (16) 28299
22.4%
Other Punctuation
ValueCountFrequency (%)
/ 10783
80.6%
. 2592
 
19.4%
Dash Punctuation
ValueCountFrequency (%)
- 5184
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 126609
85.5%
Common 21549
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 17783
14.0%
e 16877
13.3%
r 12698
10.0%
s 11892
9.4%
o 10033
 
7.9%
n 9527
 
7.5%
a 8196
 
6.5%
m 4047
 
3.2%
f 3694
 
2.9%
w 3563
 
2.8%
Other values (16) 28299
22.4%
Common
ValueCountFrequency (%)
/ 10783
50.0%
- 5184
24.1%
_ 2990
 
13.9%
. 2592
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 17783
12.0%
e 16877
11.4%
r 12698
 
8.6%
s 11892
 
8.0%
/ 10783
 
7.3%
o 10033
 
6.8%
n 9527
 
6.4%
a 8196
 
5.5%
- 5184
 
3.5%
m 4047
 
2.7%
Other values (20) 41138
27.8%

Line
Real number (ℝ)

High correlation 

Distinct973
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.38002
Minimum7
Maximum2698
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:45.667007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile31.55
Q1113.75
median253
Q3530
95-th percentile1358.45
Maximum2698
Range2691
Interquartile range (IQR)416.25

Descriptive statistics

Standard deviation435.6177
Coefficient of variation (CV)1.0799189
Kurtosis6.0460305
Mean403.38002
Median Absolute Deviation (MAD)172
Skewness2.2264769
Sum1045561
Variance189762.78
MonotonicityNot monotonic
2024-10-22T20:17:45.780913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 15
 
0.6%
63 12
 
0.5%
39 12
 
0.5%
77 11
 
0.4%
89 11
 
0.4%
82 10
 
0.4%
27 10
 
0.4%
24 10
 
0.4%
149 10
 
0.4%
52 10
 
0.4%
Other values (963) 2481
95.7%
ValueCountFrequency (%)
7 3
0.1%
8 2
 
0.1%
9 2
 
0.1%
10 1
 
< 0.1%
12 4
0.2%
13 2
 
0.1%
14 3
0.1%
15 5
0.2%
16 4
0.2%
17 5
0.2%
ValueCountFrequency (%)
2698 1
< 0.1%
2693 1
< 0.1%
2660 1
< 0.1%
2654 1
< 0.1%
2643 1
< 0.1%
2637 1
< 0.1%
2624 1
< 0.1%
2618 1
< 0.1%
2606 1
< 0.1%
2592 1
< 0.1%

Column
Categorical

Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
5
2414 
13
 
144
9
 
29
17
 
5

Length

Max length2
Median length1
Mean length1.0574846
Min length1

Characters and Unicode

Total characters2741
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 2414
93.1%
13 144
 
5.6%
9 29
 
1.1%
17 5
 
0.2%

Length

2024-10-22T20:17:45.883800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:45.967166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5 2414
93.1%
13 144
 
5.6%
9 29
 
1.1%
17 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
5 2414
88.1%
1 149
 
5.4%
3 144
 
5.3%
9 29
 
1.1%
7 5
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2741
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2414
88.1%
1 149
 
5.4%
3 144
 
5.3%
9 29
 
1.1%
7 5
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2741
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2414
88.1%
1 149
 
5.4%
3 144
 
5.3%
9 29
 
1.1%
7 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2414
88.1%
1 149
 
5.4%
3 144
 
5.3%
9 29
 
1.1%
7 5
 
0.2%

EndLine
Real number (ℝ)

High correlation 

Distinct1000
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.7473
Minimum10
Maximum2721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:46.055467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q1122.75
median262
Q3542.25
95-th percentile1364.45
Maximum2721
Range2711
Interquartile range (IQR)419.5

Descriptive statistics

Standard deviation436.94277
Coefficient of variation (CV)1.0586205
Kurtosis5.9516325
Mean412.7473
Median Absolute Deviation (MAD)173.5
Skewness2.2083276
Sum1069841
Variance190918.98
MonotonicityNot monotonic
2024-10-22T20:17:46.173942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 13
 
0.5%
46 10
 
0.4%
33 10
 
0.4%
81 10
 
0.4%
43 10
 
0.4%
147 10
 
0.4%
21 10
 
0.4%
87 10
 
0.4%
78 10
 
0.4%
250 10
 
0.4%
Other values (990) 2489
96.0%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 3
0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 3
0.1%
17 2
 
0.1%
18 5
0.2%
19 6
0.2%
ValueCountFrequency (%)
2721 1
< 0.1%
2705 1
< 0.1%
2691 1
< 0.1%
2673 1
< 0.1%
2652 1
< 0.1%
2647 1
< 0.1%
2635 1
< 0.1%
2630 1
< 0.1%
2611 1
< 0.1%
2599 1
< 0.1%

EndColumn
Real number (ℝ)

Distinct104
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.831019
Minimum4
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:46.283714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q130
median43
Q358
95-th percentile79
Maximum135
Range131
Interquartile range (IQR)28

Descriptive statistics

Standard deviation21.41287
Coefficient of variation (CV)0.47763514
Kurtosis0.14709299
Mean44.831019
Median Absolute Deviation (MAD)15
Skewness0.3714208
Sum116202
Variance458.51099
MonotonicityNot monotonic
2024-10-22T20:17:46.400341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 191
 
7.4%
42 100
 
3.9%
46 76
 
2.9%
40 70
 
2.7%
58 59
 
2.3%
51 57
 
2.2%
38 57
 
2.2%
33 51
 
2.0%
20 50
 
1.9%
25 49
 
1.9%
Other values (94) 1832
70.7%
ValueCountFrequency (%)
4 1
 
< 0.1%
9 191
7.4%
10 17
 
0.7%
12 27
 
1.0%
13 2
 
0.1%
14 2
 
0.1%
16 8
 
0.3%
17 23
 
0.9%
18 22
 
0.8%
19 33
 
1.3%
ValueCountFrequency (%)
135 3
0.1%
121 2
0.1%
117 1
 
< 0.1%
115 1
 
< 0.1%
113 3
0.1%
112 1
 
< 0.1%
109 1
 
< 0.1%
108 4
0.2%
107 1
 
< 0.1%
106 2
0.1%

CD
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:46.500341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:46.572494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

CLOC
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2229938
Minimum0
Maximum25
Zeros1723
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:46.647018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3660657
Coefficient of variation (CV)1.9346506
Kurtosis17.643323
Mean1.2229938
Median Absolute Deviation (MAD)0
Skewness3.3433568
Sum3170
Variance5.5982669
MonotonicityNot monotonic
2024-10-22T20:17:46.739155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1723
66.5%
3 284
 
11.0%
1 173
 
6.7%
4 160
 
6.2%
2 75
 
2.9%
5 60
 
2.3%
6 38
 
1.5%
7 23
 
0.9%
9 14
 
0.5%
8 9
 
0.3%
Other values (11) 33
 
1.3%
ValueCountFrequency (%)
0 1723
66.5%
1 173
 
6.7%
2 75
 
2.9%
3 284
 
11.0%
4 160
 
6.2%
5 60
 
2.3%
6 38
 
1.5%
7 23
 
0.9%
8 9
 
0.3%
9 14
 
0.5%
ValueCountFrequency (%)
25 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
17 2
 
0.1%
16 3
0.1%
15 5
0.2%
14 6
0.2%
13 3
0.1%
12 1
 
< 0.1%
11 5
0.2%

DLOC
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87152778
Minimum0
Maximum20
Zeros1978
Zeros (%)76.3%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:46.833057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8099833
Coefficient of variation (CV)2.0767936
Kurtosis13.633318
Mean0.87152778
Median Absolute Deviation (MAD)0
Skewness2.8697835
Sum2259
Variance3.2760397
MonotonicityNot monotonic
2024-10-22T20:17:46.922204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1978
76.3%
3 299
 
11.5%
4 176
 
6.8%
1 44
 
1.7%
5 33
 
1.3%
6 21
 
0.8%
2 10
 
0.4%
7 10
 
0.4%
8 5
 
0.2%
11 4
 
0.2%
Other values (5) 12
 
0.5%
ValueCountFrequency (%)
0 1978
76.3%
1 44
 
1.7%
2 10
 
0.4%
3 299
 
11.5%
4 176
 
6.8%
5 33
 
1.3%
6 21
 
0.8%
7 10
 
0.4%
8 5
 
0.2%
9 4
 
0.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
15 3
 
0.1%
14 2
 
0.1%
11 4
 
0.2%
10 2
 
0.1%
9 4
 
0.2%
8 5
 
0.2%
7 10
 
0.4%
6 21
0.8%
5 33
1.3%

LLOC
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6037809
Minimum2
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:47.030552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median5
Q39
95-th percentile21
Maximum150
Range148
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.3588026
Coefficient of variation (CV)1.0992956
Kurtosis54.748223
Mean7.6037809
Median Absolute Deviation (MAD)3
Skewness5.3366385
Sum19709
Variance69.86958
MonotonicityNot monotonic
2024-10-22T20:17:47.147334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 493
19.0%
5 294
11.3%
3 293
11.3%
4 288
11.1%
6 220
8.5%
7 165
 
6.4%
9 134
 
5.2%
8 124
 
4.8%
10 101
 
3.9%
12 67
 
2.6%
Other values (46) 413
15.9%
ValueCountFrequency (%)
2 493
19.0%
3 293
11.3%
4 288
11.1%
5 294
11.3%
6 220
8.5%
7 165
 
6.4%
8 124
 
4.8%
9 134
 
5.2%
10 101
 
3.9%
11 52
 
2.0%
ValueCountFrequency (%)
150 1
< 0.1%
113 1
< 0.1%
92 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
69 1
< 0.1%
66 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%
52 1
< 0.1%

LOC
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5783179
Minimum2
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:47.262836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median7
Q312
95-th percentile27
Maximum150
Range148
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.483637
Coefficient of variation (CV)1.0945175
Kurtosis33.280855
Mean9.5783179
Median Absolute Deviation (MAD)4
Skewness4.327906
Sum24827
Variance109.90664
MonotonicityNot monotonic
2024-10-22T20:17:47.372134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 419
16.2%
5 239
 
9.2%
6 217
 
8.4%
3 216
 
8.3%
4 201
 
7.8%
8 174
 
6.7%
7 140
 
5.4%
9 132
 
5.1%
10 113
 
4.4%
12 110
 
4.2%
Other values (60) 631
24.3%
ValueCountFrequency (%)
2 419
16.2%
3 216
8.3%
4 201
7.8%
5 239
9.2%
6 217
8.4%
7 140
 
5.4%
8 174
6.7%
9 132
 
5.1%
10 113
 
4.4%
11 71
 
2.7%
ValueCountFrequency (%)
150 1
< 0.1%
143 1
< 0.1%
99 1
< 0.1%
91 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
74 2
0.1%
73 1
< 0.1%
72 1
< 0.1%
70 1
< 0.1%

McCC
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.597608
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:47.472309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum28
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7309703
Coefficient of variation (CV)1.0834762
Kurtosis44.238433
Mean1.597608
Median Absolute Deviation (MAD)0
Skewness5.4381357
Sum4141
Variance2.9962583
MonotonicityNot monotonic
2024-10-22T20:17:47.565885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 2028
78.2%
2 246
 
9.5%
3 127
 
4.9%
4 59
 
2.3%
5 44
 
1.7%
6 24
 
0.9%
8 18
 
0.7%
7 17
 
0.7%
13 5
 
0.2%
10 4
 
0.2%
Other values (8) 20
 
0.8%
ValueCountFrequency (%)
1 2028
78.2%
2 246
 
9.5%
3 127
 
4.9%
4 59
 
2.3%
5 44
 
1.7%
6 24
 
0.9%
7 17
 
0.7%
8 18
 
0.7%
9 4
 
0.2%
10 4
 
0.2%
ValueCountFrequency (%)
28 1
 
< 0.1%
19 1
 
< 0.1%
17 2
 
0.1%
16 2
 
0.1%
14 3
0.1%
13 5
0.2%
12 4
0.2%
11 3
0.1%
10 4
0.2%
9 4
0.2%

NII
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:47.662696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:47.735501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

NL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:47.812795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:47.882710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

NLE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:47.964081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:48.032831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

NOI
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25540123
Minimum0
Maximum9
Zeros2154
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:48.105923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70201903
Coefficient of variation (CV)2.7486908
Kurtosis27.651881
Mean0.25540123
Median Absolute Deviation (MAD)0
Skewness4.3276639
Sum662
Variance0.49283072
MonotonicityNot monotonic
2024-10-22T20:17:48.180995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2154
83.1%
1 304
 
11.7%
2 84
 
3.2%
3 30
 
1.2%
4 10
 
0.4%
5 5
 
0.2%
7 2
 
0.1%
6 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 2154
83.1%
1 304
 
11.7%
2 84
 
3.2%
3 30
 
1.2%
4 10
 
0.4%
5 5
 
0.2%
6 2
 
0.1%
7 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 2
 
0.1%
6 2
 
0.1%
5 5
 
0.2%
4 10
 
0.4%
3 30
 
1.2%
2 84
 
3.2%
1 304
 
11.7%
0 2154
83.1%

NOS
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4243827
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:48.275469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile12
Maximum65
Range64
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.6005713
Coefficient of variation (CV)1.0398222
Kurtosis29.053966
Mean4.4243827
Median Absolute Deviation (MAD)2
Skewness4.1013497
Sum11468
Variance21.165256
MonotonicityNot monotonic
2024-10-22T20:17:48.380845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 644
24.8%
3 375
14.5%
4 355
13.7%
2 335
12.9%
5 253
 
9.8%
6 152
 
5.9%
7 102
 
3.9%
8 89
 
3.4%
9 70
 
2.7%
10 56
 
2.2%
Other values (28) 161
 
6.2%
ValueCountFrequency (%)
1 644
24.8%
2 335
12.9%
3 375
14.5%
4 355
13.7%
5 253
 
9.8%
6 152
 
5.9%
7 102
 
3.9%
8 89
 
3.4%
9 70
 
2.7%
10 56
 
2.2%
ValueCountFrequency (%)
65 1
 
< 0.1%
45 2
0.1%
44 2
0.1%
41 1
 
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
33 3
0.1%
30 3
0.1%
29 1
 
< 0.1%

NUMPAR
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7021605
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:48.466892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.140813
Coefficient of variation (CV)0.67021469
Kurtosis7.2642286
Mean1.7021605
Median Absolute Deviation (MAD)0
Skewness2.121133
Sum4412
Variance1.3014542
MonotonicityNot monotonic
2024-10-22T20:17:48.555869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1633
63.0%
2 456
 
17.6%
3 238
 
9.2%
4 221
 
8.5%
5 21
 
0.8%
7 13
 
0.5%
6 7
 
0.3%
13 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 1633
63.0%
2 456
 
17.6%
3 238
 
9.2%
4 221
 
8.5%
5 21
 
0.8%
6 7
 
0.3%
7 13
 
0.5%
8 1
 
< 0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 13
 
0.5%
6 7
 
0.3%
5 21
 
0.8%
4 221
 
8.5%
3 238
 
9.2%
2 456
 
17.6%
1 1633
63.0%

TCD
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:48.647223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:48.716501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

TCLOC
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2534722
Minimum0
Maximum30
Zeros1707
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:48.783298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4416359
Coefficient of variation (CV)1.9478979
Kurtosis22.07599
Mean1.2534722
Median Absolute Deviation (MAD)0
Skewness3.6276247
Sum3249
Variance5.9615858
MonotonicityNot monotonic
2024-10-22T20:17:48.880440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 1707
65.9%
3 283
 
10.9%
1 179
 
6.9%
4 158
 
6.1%
2 80
 
3.1%
5 62
 
2.4%
6 40
 
1.5%
7 24
 
0.9%
9 14
 
0.5%
8 11
 
0.4%
Other values (12) 34
 
1.3%
ValueCountFrequency (%)
0 1707
65.9%
1 179
 
6.9%
2 80
 
3.1%
3 283
 
10.9%
4 158
 
6.1%
5 62
 
2.4%
6 40
 
1.5%
7 24
 
0.9%
8 11
 
0.4%
9 14
 
0.5%
ValueCountFrequency (%)
30 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
17 2
 
0.1%
16 3
0.1%
15 5
0.2%
14 6
0.2%
13 3
0.1%
12 1
 
< 0.1%

TLLOC
Real number (ℝ)

High correlation 

Distinct58
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3252315
Minimum2
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:48.982784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median6
Q310
95-th percentile23
Maximum150
Range148
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.9098324
Coefficient of variation (CV)1.0702204
Kurtosis47.449684
Mean8.3252315
Median Absolute Deviation (MAD)3
Skewness4.9585428
Sum21579
Variance79.385114
MonotonicityNot monotonic
2024-10-22T20:17:49.089101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 452
17.4%
5 288
11.1%
3 264
10.2%
4 234
9.0%
6 226
8.7%
9 154
 
5.9%
7 151
 
5.8%
8 125
 
4.8%
10 108
 
4.2%
12 84
 
3.2%
Other values (48) 506
19.5%
ValueCountFrequency (%)
2 452
17.4%
3 264
10.2%
4 234
9.0%
5 288
11.1%
6 226
8.7%
7 151
 
5.8%
8 125
 
4.8%
9 154
 
5.9%
10 108
 
4.2%
11 62
 
2.4%
ValueCountFrequency (%)
150 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
78 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
66 2
0.1%
58 1
< 0.1%
55 1
< 0.1%
52 2
0.1%

TLOC
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.367284
Minimum2
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:49.205934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median7
Q313
95-th percentile29
Maximum150
Range148
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.159632
Coefficient of variation (CV)1.0764277
Kurtosis29.226196
Mean10.367284
Median Absolute Deviation (MAD)4
Skewness4.0512309
Sum26872
Variance124.53738
MonotonicityNot monotonic
2024-10-22T20:17:49.316645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 400
15.4%
5 211
 
8.1%
3 206
 
7.9%
6 190
 
7.3%
4 187
 
7.2%
8 161
 
6.2%
7 151
 
5.8%
9 116
 
4.5%
12 113
 
4.4%
10 110
 
4.2%
Other values (63) 747
28.8%
ValueCountFrequency (%)
2 400
15.4%
3 206
7.9%
4 187
7.2%
5 211
8.1%
6 190
7.3%
7 151
 
5.8%
8 161
6.2%
9 116
 
4.5%
10 110
 
4.2%
11 79
 
3.0%
ValueCountFrequency (%)
150 1
< 0.1%
143 1
< 0.1%
124 1
< 0.1%
91 1
< 0.1%
89 1
< 0.1%
84 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
74 1
< 0.1%

TNOS
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5451389
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2024-10-22T20:17:49.422047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile12
Maximum65
Range64
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.6789113
Coefficient of variation (CV)1.029432
Kurtosis27.197921
Mean4.5451389
Median Absolute Deviation (MAD)2
Skewness3.9568193
Sum11781
Variance21.892211
MonotonicityNot monotonic
2024-10-22T20:17:49.522754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 630
24.3%
3 362
14.0%
4 354
13.7%
2 328
12.7%
5 247
 
9.5%
6 144
 
5.6%
7 126
 
4.9%
8 95
 
3.7%
9 71
 
2.7%
10 63
 
2.4%
Other values (28) 172
 
6.6%
ValueCountFrequency (%)
1 630
24.3%
2 328
12.7%
3 362
14.0%
4 354
13.7%
5 247
 
9.5%
6 144
 
5.6%
7 126
 
4.9%
8 95
 
3.7%
9 71
 
2.7%
10 63
 
2.4%
ValueCountFrequency (%)
65 1
 
< 0.1%
45 2
0.1%
44 2
0.1%
41 1
 
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
33 4
0.2%
30 3
0.1%
29 1
 
< 0.1%

WarningBlocker
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:49.629462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:49.700262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

WarningCritical
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:49.779579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:49.866368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

WarningInfo
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2526 
1
 
58
2
 
5
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2526
97.5%
1 58
 
2.2%
2 5
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Length

2024-10-22T20:17:49.955798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:50.038961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2526
97.5%
1 58
 
2.2%
2 5
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2526
97.5%
1 58
 
2.2%
2 5
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2526
97.5%
1 58
 
2.2%
2 5
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2526
97.5%
1 58
 
2.2%
2 5
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2526
97.5%
1 58
 
2.2%
2 5
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

WarningMajor
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:50.131150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:50.200754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

WarningMinor
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:50.272610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:50.347384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

Anti Pattern
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2551 
1
 
39
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2551
98.4%
1 39
 
1.5%
2 2
 
0.1%

Length

2024-10-22T20:17:50.415413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:50.497650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2551
98.4%
1 39
 
1.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2551
98.4%
1 39
 
1.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2551
98.4%
1 39
 
1.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2551
98.4%
1 39
 
1.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2551
98.4%
1 39
 
1.5%
2 2
 
0.1%

Complexity Metric Rules
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2571 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2571
99.2%
1 21
 
0.8%

Length

2024-10-22T20:17:50.579396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:50.649671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2571
99.2%
1 21
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2571
99.2%
1 21
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2571
99.2%
1 21
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2571
99.2%
1 21
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2571
99.2%
1 21
 
0.8%

Coupling Metric Rules
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:50.728985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:50.799994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

Documentation Metric Rules
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
100.0%

Length

2024-10-22T20:17:50.872242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:50.947098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2592
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2592
100.0%

Size Metric Rules
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
0
2562 
1
 
27
3
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2562
98.8%
1 27
 
1.0%
3 2
 
0.1%
2 1
 
< 0.1%

Length

2024-10-22T20:17:51.016598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T20:17:51.097730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2562
98.8%
1 27
 
1.0%
3 2
 
0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2562
98.8%
1 27
 
1.0%
3 2
 
0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2562
98.8%
1 27
 
1.0%
3 2
 
0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2562
98.8%
1 27
 
1.0%
3 2
 
0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2562
98.8%
1 27
 
1.0%
3 2
 
0.1%
2 1
 
< 0.1%

Interactions

2024-10-22T20:17:41.048807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:24.662353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.829268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.981551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.181865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.317005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.552980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.740172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.929740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.106132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.221332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.374722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.514762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.673629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.853209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.130083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:24.737741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.907137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.060819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.254563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.395513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.622990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.818455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.001686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.182326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.296638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.448162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.585690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.751458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.929704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.218164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:24.808963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.978880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.134551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.327248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.472133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.706812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.899001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.077552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.254949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.367759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.514456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.663149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.829791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.002630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.299137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:24.886136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.053778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.215996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.406768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.560442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.788480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.980460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.153626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.331975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.448895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.600551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.745854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.905140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.086451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.378742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:24.965209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.133934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.297247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.472383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.634652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.864454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.052790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.229339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.395780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.519695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.666483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.814144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.981488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.167732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.465135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.047339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.216870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.384901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.557219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.721492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.952209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.138248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.312171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.485040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.610482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.747391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.899575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.069525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.254630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.539328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.124543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.295480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.466158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.635519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.811182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.034119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.224076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.392112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.565102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.685081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.830842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.984516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.148095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.333239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.619624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.208180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.373995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.546508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.719355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.895535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.116921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.301887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.483626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.640698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.763467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.914631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.064957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.232099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.417610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.698113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.283370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.448787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.624290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.786504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.982220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.192543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.382691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.571725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.712498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.838620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.984758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.131881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.303449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.489465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.764873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.362557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.515744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.699549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.857465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.058299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.264996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.453727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.646372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.779774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.912935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.051491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.208169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.380938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.566806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.847418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.435298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.593906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.778633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.934782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.141217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.339246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.534552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.718118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.853541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.985531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.131280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.285067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.454527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.646194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.922542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.516004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.667300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.851441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.008029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.214491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.421271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.610808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.798022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.921163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.064278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.204382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.357398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.535520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.723036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:41.999064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.588543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.739730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:27.932089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.083378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.301930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.496341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.690287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.870171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.997675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.140355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.280615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.430634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.611507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.799583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:42.079558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.670825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.825400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.017570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.162006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.385869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.583167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.771155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:33.954124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.074027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.219343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.360490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.513954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.688571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.881551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:42.157239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:25.749424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:26.906774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:28.098214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:29.241048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:30.471621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:31.665586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:32.851580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:34.033836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:35.146301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:36.298983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:37.439985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:38.597071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:39.772440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-22T20:17:40.964315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-22T20:17:51.166167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Anti PatternCLOCColumnComplexity Metric RulesDLOCEndColumnEndLineLLOCLOCLineMcCCNOINOSNUMPARSize Metric RulesTCLOCTLLOCTLOCTNOSWarningInfo
Anti Pattern1.0000.2540.0000.7290.0830.0610.0310.3310.3590.0350.5940.0800.4910.7870.4220.2420.3150.3430.4760.670
CLOC0.2541.0000.0650.4970.8510.0010.0330.3070.5420.0150.2950.1330.3290.0040.1000.9910.2830.5020.3230.189
Column0.0000.0651.0000.0000.0560.0760.0900.0000.0280.0950.0000.0400.0370.0220.0000.0150.0000.0370.0350.000
Complexity Metric Rules0.7290.4970.0001.0000.1460.0510.0820.4890.4810.0870.9570.1790.8060.3230.1900.4550.4340.4940.7830.603
DLOC0.0830.8510.0560.1461.0000.019-0.0060.1970.433-0.0200.1850.0940.201-0.0140.0000.8450.1860.4020.1980.037
EndColumn0.0610.0010.0760.0510.0191.000-0.005-0.094-0.054-0.002-0.174-0.0390.057-0.2270.0000.007-0.059-0.0320.0590.034
EndLine0.0310.0330.0900.082-0.006-0.0051.0000.2000.1940.9990.0730.1120.174-0.0730.0000.0400.2270.2130.1770.000
LLOC0.3310.3070.0000.4890.197-0.0940.2001.0000.9350.1710.4110.2780.862-0.1210.8450.3110.9400.8900.8500.756
LOC0.3590.5420.0280.4810.433-0.0540.1940.9351.0000.1630.4120.2730.830-0.1250.7210.5460.9140.9680.8270.551
Line0.0350.0150.0950.087-0.020-0.0020.9990.1710.1631.0000.0590.1030.149-0.0710.0000.0210.1970.1810.1520.000
McCC0.5940.2950.0000.9570.185-0.1740.0730.4110.4120.0591.0000.2660.4610.3960.4160.2910.3560.3700.4490.583
NOI0.0800.1330.0400.1790.094-0.0390.1120.2780.2730.1030.2661.0000.2450.1090.0810.1310.2620.2620.2420.102
NOS0.4910.3290.0370.8060.2010.0570.1740.8620.8300.1490.4610.2451.000-0.1140.4110.3300.8120.7900.9870.553
NUMPAR0.7870.0040.0220.323-0.014-0.227-0.073-0.121-0.125-0.0710.3960.109-0.1141.0000.530-0.002-0.179-0.166-0.1240.433
Size Metric Rules0.4220.1000.0000.1900.0000.0000.0000.8450.7210.0000.4160.0810.4110.5301.0000.1030.6740.8310.4100.752
TCLOC0.2420.9910.0150.4550.8450.0070.0400.3110.5460.0210.2910.1310.330-0.0020.1031.0000.2960.5130.3270.182
TLLOC0.3150.2830.0000.4340.186-0.0590.2270.9400.9140.1970.3560.2620.812-0.1790.6740.2961.0000.9520.8340.639
TLOC0.3430.5020.0370.4940.402-0.0320.2130.8900.9680.1810.3700.2620.790-0.1660.8310.5130.9521.0000.8120.639
TNOS0.4760.3230.0350.7830.1980.0590.1770.8500.8270.1520.4490.2420.987-0.1240.4100.3270.8340.8121.0000.558
WarningInfo0.6700.1890.0000.6030.0370.0340.0000.7560.5510.0000.5830.1020.5530.4330.7520.1820.6390.6390.5581.000

Missing values

2024-10-22T20:17:42.307636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-22T20:17:42.673849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDNameLongNameParentComponentPathLineColumnEndLineEndColumnCDCLOCDLOCLLOCLOCMcCCNIINLNLENOINOSNUMPARTCDTCLOCTLLOCTLOCTNOSWarningBlockerWarningCriticalWarningInfoWarningMajorWarningMinorAnti PatternComplexity Metric RulesCoupling Metric RulesDocumentation Metric RulesSize Metric Rules
0L184readyapps.RestFrameworkConfig.ready~FnL177L103/content/django-rest-framework/rest_framework/apps.py851051020231000011022310000000000
1L197_rejectauthentication.CSRFCheck._reject~FnL195L103/content/django-rest-framework/rest_framework/authentication.py2853021010231000013012310000000000
2L206authenticateauthentication.BaseAuthentication.authenticate~FnL203L103/content/django-rest-framework/rest_framework/authentication.py3854272033251000012032510000000000
3L212authenticate_headerauthentication.BaseAuthentication.authenticate_header~FnL203L103/content/django-rest-framework/rest_framework/authentication.py4455012055271000012052710000000000
4L220authenticateauthentication.BasicAuthentication.authenticate~FnL216L103/content/django-rest-framework/rest_framework/authentication.py5958771044202970002172042029170000000000
5L227authenticate_credentialsauthentication.BasicAuthentication.authenticate_credentials~FnL216L103/content/django-rest-framework/rest_framework/authentication.py895106270441118300007404111870000000000
6L234authenticate_headerauthentication.BasicAuthentication.authenticate_header~FnL216L103/content/django-rest-framework/rest_framework/authentication.py108510963000221000012002210000000000
7L241authenticateauthentication.SessionAuthentication.authenticate~FnL238L103/content/django-rest-framework/rest_framework/authentication.py11751332707461730001520761750000000000
8L247enforce_csrfauthentication.SessionAuthentication.enforce_csrf~FnL238L103/content/django-rest-framework/rest_framework/authentication.py13551487305361220000520681460000000000
9L259get_modelauthentication.TokenAuthentication.get_model~FnL254L103/content/django-rest-framework/rest_framework/authentication.py164516820000552000041005540000000000
IDNameLongNameParentComponentPathLineColumnEndLineEndColumnCDCLOCDLOCLLOCLOCMcCCNIINLNLENOINOSNUMPARTCDTCLOCTLLOCTLOCTNOSWarningBlockerWarningCriticalWarningInfoWarningMajorWarningMinorAnti PatternComplexity Metric RulesCoupling Metric RulesDocumentation Metric RulesSize Metric Rules
2582L16582gettest_throttling.ScopedRateThrottleTests.setUp.YView.get~FnL16578L103/content/django-rest-framework/tests/test_throttling.py2811328236000221000012002210000000000
2583L16590gettest_throttling.ScopedRateThrottleTests.setUp.UnscopedView.get~FnL16587L103/content/django-rest-framework/tests/test_throttling.py2871328836000221000012002210000000000
2584L16620timertest_throttling.XffTestingBase.setUp.Throttle.timer~FnL16616L103/content/django-rest-framework/tests/test_throttling.py3821338341000221000011002210000000000
2585L16629gettest_throttling.XffTestingBase.setUp.View.get~FnL16625L103/content/django-rest-framework/tests/test_throttling.py3891339045000221000012002210000000000
2586L17350filtertest_validators.TestUniquenessTogetherValidation.test_filter_queryset_do_not_skip_existing_attribute.MockQueryset.filter~FnL17348L103/content/django-rest-framework/tests/test_validators.py5001350141000221000012002210000000000
2587L17503existstest_validators.ValidatorsTests.test_qs_exists_handles_type_error.TypeErrorQueryset.exists~FnL17501L103/content/django-rest-framework/tests/test_validators.py8081380931000221000011002210000000000
2588L17513existstest_validators.ValidatorsTests.test_qs_exists_handles_value_error.ValueErrorQueryset.exists~FnL17511L103/content/django-rest-framework/tests/test_validators.py8141381532000221000011002210000000000
2589L17521existstest_validators.ValidatorsTests.test_qs_exists_handles_data_error.DataErrorQueryset.exists~FnL17519L103/content/django-rest-framework/tests/test_validators.py8201382131000221000011002210000000000
2590L17677gettest_versioning.TestHyperlinkedRelatedField.setUp.MockQueryset.get~FnL17675L103/content/django-rest-framework/tests/test_versioning.py3351333639000221000012002210000000000
2591L17969wrapped_list_actiontest_viewsets.GetExtraActionsTests.test_attr_name_check.ActionViewSet.wrapped_list_action~FnL17966L103/content/django-rest-framework/tests/test_viewsets.py2471325041000441000014004410000000000